The present invention generally relates to fingerprinting positioning technologies which is now widely used and also discussed e.g. during 3GPP standardization. Also known as e.g. “radio pattern matching” or “radio signature”, fingerprinting positioning technologies represent a family of substantially path loss based technologies that rely on matching the Radio Frequency (RF) environment, as experienced by the User Equipment (UE), to the known characteristics of the larger RF system in which the UE is operating. Information from the UE, including measurements of neighbour cell signal strengths, time delay and other network parameters form the basis of the RF environment to be compared to the established system RF database. The intent of this approach is to mitigate the negative impacts of anomalies within the RF environment that challenge the accuracy of trilateration technologies, e.g. multipath and reflection.
The RF fingerprinting positioning method is based on measurements made by the UE and the Radio Base Station (RBS). The essential measurement set required for such method is currently defined in 3GPP TS 25.215 [1] and necessary for the basic mobility functionality and hence this method will work with existing mobiles without any modification.
There are growing market segments for location services that require both location accuracy and user transparency. Government Surveillance and Lawful Intercept put requirements on the positioning or tracking of individuals or groups by government or authority organizations, typically for the purpose of security. Due to the user transparency, these services cannot be addressed with location technologies which require UE support or modification, such as Assisted Global Positioning System (A-GPS), Global Navigation Satellite System (GNSS), and Observed Time Difference Of Arrival (OTDOA). Additionally, emergency service applications require a level of location accuracy which has not yet been met with Cell IDentity (Cell-ID) and Round Trip Time (RTT) methods. The potential benefits of RF fingerprinting and the relative ease with which this location method can be adopted in the Universal Mobile Telecommunication System (UMTS) Terrestrial Radio Access Network (UTRAN) would indicate that it is appropriate that the technology would be included in the UTRAN in support of the services noted above, as well as for cooperative deployment with satellite-based systems (A-GPS, GNSS, etc.) in support of “hybrid” location technology for Location Based Services (LBS).
Fingerprinting positioning algorithms in Global System for Mobile communications (GSM)/Wideband Code Division Multiple Access (WCDMA)/Long-Term Evolution (LTE) operate by creating a radio fingerprint for each point of a fine coordinate grid that covers the Radio Access Network (RAN). Each such measurement must be associated with an identity (ID) of a RBS. The fingerprint may e.g. comprise:                The cell IDs that are detected by the UE, in each grid point.        Quantized path loss or signal strength measurements, with respect to multiple RBSs, performed by the UE, in each grid point.        Quantized RTT, in WCDMA, or Timing Advance (TA), in GSM and LTE, or UE Receiver-Transmitter (UERx-Tx) time difference (in LTE) in each grid point.        Quantized noise rise, representing the load of a Code Division Multiple Access (CDMA) system, in each grid point.        Quantized signal quality e.g. Received signal Quality (RxQual) in GSM, Ec/NO in WCDMA and Reference Signal Received Quality (RSRQ) in LTE.        Radio connection information like the Radio Access Bearer (RAB).        Quantized time.        
Whenever a position request arrives, a radio fingerprint is first measured, after which the corresponding grid points with similar characteristic are looked up and a location estimate is calculated and reported.
Adaptive Enhanced Cell ID (AECID) is one kind of fingerprinting positioning technology that refines the basic cell identity positioning method in a variety of ways, see e.g. T. Wigren in “Adaptive enhanced cell ID fingerprinting localization by clustering of precise position measurements”, IEEE Trans. Veh. Tech., vol. 56, pp. 3199-3209, 2007 [2]. The AECID positioning method is based on the idea that high precision positioning measurements, e.g. A-GPS measurements, can be seen as points that belong to regions where certain cellular radio propagation condition persist. The preparation of a database for AECID positioning is performed in three basic steps.
In a first step, A-GPS positioning, or any other available high-precision positioning, is performed at the same time as UE network signal measurement. The AECID positioning method introduces a tagging of the high precision measurements according to certain criteria, e.g. including:                The cell IDs that are detected by the UE.        Quantized path loss or signal strength measurements, with respect to multiple RBSs, performed by the UE.        Quantized RTT, TA or UE Rx-Tx time difference.        Quantized noise rise.        Quantized signal quality e.g. RxQual, Ec/NO or RSRQ.        Radio connection information like the RAB.        Quantized time.        
It is important to note that the tag consist of a vector of indices, where each index taking an enumerable number of discrete values. Continuous variables used for tagging, like path loss, hence need to be quantized.
In a second step, all high precision positioning measurements that have the same tag are collected in separate high precision measurement clusters. Further processing of the clusters is performed in order to refine the position definition, i.e. an associated geographical region. The geographical regions can be smaller than the extension of a cell of the cellular system.
In a third step, a polygon that represents the geographical extension of a cluster is computed for each stored high precision position measurement cluster. The two most pronounced properties of this particular algorithm include that the area of the polygon is minimized, i.e. that the accuracy hence is maximized, and that the probability that the terminal is within the polygon, i.e. the confidence, is precisely known, since it is set as a constraint in the algorithm.
For an incoming positioning request, the UE's network measurement is first obtained. By looking up cell IDs or tags, the polygon corresponding to the determined tag is then looked up in the tagged database of polygons, followed by a reporting, e.g. over the Radio Access Network Application Part (RANAP) using the polygon format.
The accuracy of AECID positioning could be affected by a number of factors. The criteria of tagging is one important parameter. For example, tagging with serving cell ID and TA is expected to provide more information than tagging only with serving cell ID. This would thus provide higher accuracy. The selection of criteria may vary from implementations to implementations.
The quality of the collected high accuracy positioning measurements used to generate the tagged polygons is also of importance. In ideal cases, it is expected that those high accuracy positioning measurements are evenly distributed over the whole target area, but in real cases, it is quite difficult to fulfil this. Usually, a driving test is performed and the operator uses tools such as e.g. TEMS™ to collect data along the road. The collected data is then used for AECID. Thus, the data collection frequency, i.e. how much data is collected every second, or the density of roads available for cars may highly affect the expected accuracy of AECID.
The distribution of the cells and the RBS sites within the cells is also of importance. It is intuitive that the smaller the Inter Site Distance (ISD) between sites is, the high accuracy is expected. Another aspect might be that the area covered by sectored-cells might rise to higher accuracy than the area covered by omni-cells. For instance, AECID is expected to perform better in urban areas than rural areas.
All these aspects make it difficult to foresee the possible accuracy that might be achieved by fingerprinting positioning with a certain cell planning in a certain area. An operator thus has large difficulties in deciding whether or not it is sufficient to deploy fingerprinting positioning methods, such as AECID, to fulfil accuracy requirements, unless the positioning method is implemented and the actual achieved accuracy is measured. Questions like whether or not the AECID should be deployed only in urban area, or how many roads that shall be covered to achieve a certain accuracy, are difficult to answer beforehand.